profiler.decor(model)
-for k = 1, 10 do
- local input = torch.Tensor(1000, 1000):uniform(-1, 1)
- local target = torch.Tensor(input:size(1), 100):uniform()
- local criterion = nn.MSECriterion()
+local input = torch.Tensor(1000, 1000)
+local target = torch.Tensor(input:size(1), 100)
+local criterion = nn.MSECriterion()
+
+local nbSamples = 0
+local modelTime = 0
+local dataTime = 0
+
+for k = 1, 5 do
+ local t1 = sys.clock()
+ input:uniform(-1, 1)
+ target:uniform()
+
+ local t2 = sys.clock()
+
local output = model:forward(input)
local loss = criterion:forward(output, target)
local dloss = criterion:backward(output, target)
model:backward(input, dloss)
+
+ local t3 = sys.clock()
+
+ dataTime = dataTime + (t2 - t1)
+ modelTime = modelTime + (t3 - t2)
+
+ nbSamples = nbSamples + input:size(1)
end
-profiler.print(model)
+profiler.print(model, nbSamples)
+
+print('----------------------------------------------------------------------')
+print(string.format('Total model time %.02fs', modelTime))
+print(string.format('Total data time %.02fs', dataTime))